1. Field of the Invention
The present invention relates to a wireless communication apparatus and a wireless communication method for performing spatially multiplexed communication using a plurality of logical channels formed by a pair of a transmitter and a receiver each having a plurality of antennas. In particular, the present invention relates to a wireless communication apparatus and a wireless communication method for a MIMO (Multiple Input Multiple Output) communication scheme in which, on the basis of channel characteristics, spatially multiplexed signals are synthesized in accordance with the MIMO scheme and are then separated into individual signals on a stream-by-stream basis.
More particularly, the present invention relates to a wireless communication apparatus and a wireless communication method for spatially decoding spatially multiplexed signals into individual stream signals by synthesizing them in accordance with the MIMO scheme, using an antenna weight matrix W obtained from an estimated channel matrix H in accordance with an MMSE (Minimum Mean Square Error) algorithm. In particular, the present invention relates to a wireless communication apparatus and a wireless communication method for performing accurate likelihood estimation in a MIMO receiver that employs the MMSE algorithm.
2. Description of the Related Art
Wireless networks have attracted attention as a system for relieving users from the necessity of using wiring according to a known wire communication scheme. One of the standards related to wireless networks is IEEE (Institute of Electrical and Electronics Engineers) 802.11.
The IEEE 802.11a standard supports a modulation scheme that achieves a maximum communication speed of 54 Mbps. However, there is a demand for a wireless standard capable of realizing a higher bit rate. As one of the methods of accelerating wireless communication, MIMO (Multi-Input Multi-Output) communication has attracted attention. MIMO communication is communication that realizes spatially multiplexed transmission channels (hereinafter referred to as “MIMO channels”) by providing both a transmitter and a receiver with a plurality of antennas. The MIMO transmitter distributes transmission data and then transmits the distributed transmission data to a plurality of antennas of the transmitter. The MIMO receiver receives space signals from a plurality of antennas and then performs signal processing on the received signals, thereby acquiring each signal without crosstalk (see, for example, Japanese Unexamined Patent Application Publication No. 2002-44051).
According to the MIMO communication scheme, transmission capacity increases with the number of antennas without the need for broadening a frequency band, thereby achieving an enhancement of communication speed. In addition, since the MIMO communication scheme employs spatial multiplexing, frequency usage efficiency can be improved. The MIMO communication scheme is a communication scheme that uses channel characteristics, thus it is not the same as a transmitting/receiving adaptive array communication scheme.
FIG. 11 is a conceptual diagram of a MIMO communication system. As illustrated in FIG. 11, a transmitter and a receiver each has a plurality of antennas. The transmitter multiplexes a plurality of pieces of transmission data by performing space-time coding on the data. The coded data is distributed to M transmitting antennas and then transmitted to MIMO channels. The receiver receives signals via the MIMO channels from N receiving antennas, and then performs space-time decoding on the received signals, thereby enabling the acquirement of received data. It is desirable that the number of MIMO streams to be formed corresponds to the smaller number of either transmitting antennas or receiving antennas (MIN[M, N]).
Generally, each MIMO channel has a configuration that includes radio wave propagation environments around a transmitter and a receiver (transfer functions) and a configuration of channel space (transfer function). Although crosstalk occurs when signals to be transmitted from each antenna are multiplexed, a receiver can correctly acquire each multiplexed signal without crosstalk by performing receiving processing.
The MIMO receiver can acquire each stream signal x by the following procedure: acquiring a channel matrix H in some way; obtaining an antenna weight matrix W using the channel matrix H in accordance with a predetermined algorithm; and multiplying the antenna weight matrix W by each spatially multiplexed received signal y. In other words, the MIMO receiver can spatially separate or spatially decode the received signals.{circumflex over (x)}=Wy  (1)
For example, the transmitter transmits a reference signal including a known training series. Using the reference signal, the receiver can acquire the channel matrix H.
As relatively simple algorithms for obtaining the antenna weight matrix W using the channel matrix H, zero-forcing and MMSE (Minimum Mean Square Error) algorithms are known. Zero-forcing is a method based on the logic of completely removing crosstalk. On the other hand, MMSE is a method based on the logic of maximizing the ratio of signal power to a square error (the sum of crosstalk power and noise power). This MMSE method introduces the concept of noise power of the receiver, wherein crosstalk is intentionally generated to obtain the antenna weight matrix W. Comparing both algorithms, it is known that the MMSE algorithm is superior in a high-noise environment.
Generally, in these zero-forcing and MMSE algorithms, the value of signal amplitude of a signal to be received after spatial decoding is obtained so as to be equal to about unity. Consequently, the value of signal amplitude of the post-spatial-decoding received signal is approximately about unity. At this point, the strength information of the received signal, that is, pseudo SNR information, is lost. Accordingly, some likelihood information needs to be provided to a soft decision decoder.
For example, in the zero-forcing, the following equation (2) is generally used to obtain a post-spatial-decoding SNR relative estimate. This equation is based on the fact that the square norm of the weight vector of each stream becomes equal to the gain of noise power with the expectation that the expected value of post-spatial-decoding signal amplitude regularly becomes equal to unity.
                                          SNR            ZF                    ⁡                      (            l            )                          =                  1                                                                                    w                  .                                l                                                    2                                              (        2        )                                                      w            .                    l                =                  [                                                                      w                                      l                    ⁢                                                                                  ⁢                    1                                                                              ⋯                                                              w                  ln                                                            ⋯                                                              w                  lN                                                              ]                                    (        3        )                                W        =                              [                                                                                                      w                      .                                                                                                                                  ⁢                      1                                                                                        ⋯                                                                                            w                      .                                        l                                                                    ⋯                                                                                            w                      .                                        L                                                                        ]                    T                                    (        4        )            l: stream serial numbern: receiving branch serial number receiving branchesW: antenna weight matrix{dot over (w)}1: antenna weight vector of l-th streamL: number of streamsN: number of receiving branches
The above equation (3) shows that an antenna weight vector of an l-th stream is a vector including an antenna weight wln between the l-th stream and each receiving branch n as an element. The above equation (4) shows that an antenna weight matrix W is a transposed matrix of the matrix including the antenna weight vector on a stream-by-stream basis as a row vector. Gain associated with spatial decoding processing in each stream is the square norm of the antenna weight vector of the stream. In the above equation (2), the SNR of the l-th stream after spatial decoding is obtained as the reciprocal number of the square norm of the antenna weight vector of the stream.
Finally, the value of the square root of the SNR estimate shown in the following equation (5) is sent to the soft decision decoder as a likelihood amplitude. As is clear from the above equation (2), such likelihood information is estimated by using only the antenna weight matrix.Y=√{square root over (SNRZF(l))}  (5)
On the other hand, in the MMSE algorithm, crosstalk (interference between streams) is intentionally generated when calculating the antenna weight matrix, and the expected value of signal amplitude after spatial decoding is not limited to unity. In view of these facts, it is considered difficult to expect accuracy from the likelihood estimate obtained by using only the antenna weight matrix as shown in the above equation (2).
This inaccurate likelihood estimation causes the degradation of decoding characteristics of the soft decision decoder, and consequently has an effect on the performance of the entire receiver.